Hospital information systems: more computer firms entering already crowded hospital market.
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With medical billing becoming more difficult and confusing, you may decide that automated billing and collecting is just the answer you have been looking for to help take the mystery out of receivables. Currently, there are a myriad of in-house systems available to the physician while only a limited number of on-line systems exist. Which computer system is right for your practice? To answer this question, you will need to answer a series of other questions first. To help define your business problem, you must fully understand your practice. The following questions will allow you to analyze your office.
Resting multigated blood-pool studies were performed on 61 patients without arrhythmias and data were simultaneously acquired to two computer systems. Using one computer, manual ejection fraction (EF) was calculated by two trained observers. EF was also calculated from the other computer using a commercially available fully automated program; quality control (QC) images were routinely obtained to evaluate correct left-ventricular center location, background region assignment, or gross edge mispositioning. When errors were noted, the automated analysis was reprocessed with operator intervention. Forty-eight of the 61 studies (78%) produced adequate QC images. Operator redefinition of the left ventricular center and background of the 13 QC failures raised the automated success level to 92%. Correlation of the manual EF by two observers was excellent (r = 0.969). The automated EF measurements correlated well with the average observer's (r = 0.898). An improved version of software reduced the QC failures from 13 to 10. Operator assisted automated processing gave the success rate of 94%. The remaining 6% of patients required manual processing to obtain a correct ejection fraction. Our normal range for manual EF is greater than or equal to 0.50 and our corresponding normal range for this group of patients using the automated program was greater than or equal to 0.44. Based on greater than or equal to 0.44, four patients with a low manual EF had a normal automated EF. All four patients had cardiac disease but there was no evidence of abnormal cardiac function. The fully automated program provided good correlation with manual EF and can remove some of the subjectivity in manual edge determination. However, QC images must be carefully examined and the normal EF range for the automated program must be determined.
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Computer technology has advanced rapidly over the past 30 years as the computer has been more and more integrated into the practice of medicine. Current horizons of research have begun to make use of this newer technology in 1986. The numerous amounts of change and advances in computer systems with respect to medicine and specifically to dermatology will make the computer even more of an integral tool for the practice of this specialty in the future.
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Regulations and standards must include the minimum requirements with respect to veterinary care, sanitation, handling, feeding, and housing. Part 1 of the Animal Welfare Act regulations was amended to update, clarify, and expand the list of definitions of terms and standards. Section 9 CFR, Part 1, contains definitions and deals with animal welfare, animal housing, dealers, exhibitors, research facilities, and humane animal handling. The subjects in 9 CFR, Part 2, pertain to licensing, registration, identification of animals, records, institutional animal care and use committees, and adequate veterinary care. Animal welfare, humane animal handling, pets, transportation, and reporting and recordkeeping requirements are the subjects listed in 9 CFR, Part 3.
A system was developed to reduce some of the difficulties associated with hydrostatic (underwater) weighing, specifically the need for complete exhalation and the subjective approximation of weighing scale measurements. The exhalation portion of the weighing protocol is particularly difficult for many disabled individuals and has contributed to the lack of available body composition information for this population. The components of our system include a computer system, load cell, spirometer, breathing tube, logic and signal conditioning circuitry specially constructed for this system, and a software package developed for this project. In a preliminary test, the body fat percentages of fourteen subjects (six males and eight females, ages 21-32 years) were determined both with the standard method and with our system. A correlation of r = 0.967 was found between the two methods, with our system's precision ranging from 1.0 to 1.3 body fat percentage points. The system could be used, for example, in developing a database for monitoring an individual's fitness or for making comparisons between groups (such as athlete to non-athlete).
PURPOSE: To measure the accuracy of laser-guided punctures and to evaluate the usefulness of the developed laser-guided system. MATERIAL AND METHODS: The study included 67 patients referred for computed-tomography-guided puncture. The majority of the punctured lesions were located in the chest (28 patients) or abdomen (19 patients). The mean diameter of the lesion was 3.1 cm, and the mean path length was 6.8 cm. The laser guide was movable along a horizontal or vertical rail and not physically connected to the CT unit. The angle of insertion was entered manually into the display of the unit by the interventional radiologist. A prospective multicenter trial was carried out. RESULTS: A mean of 1.1 needle passes were necessary to reach the target, and in 55 (84.6%) of the patients the target was reached on the first needle pass. The mean deviation of the needle from the preselected angle was 1.8 degrees. The mean targeting time (from the initial localizing scan until the needle was in the target) was 15.6 min. In every case, both the usefulness and the ease of use of the laser guidance system were subjectively evaluated on a 5-point scale; the mean usefulness score was 4.5, while the mean ease of use score was 4.7. CONCLUSION: Different users at four different centers found the laser guidance system useful and easy to use. A high level of accuracy of the puncture angle was obtained in most cases.
Sperm morphology has been identified as one characteristic which can be useful in the prediction of sperm fertility, therefore, we hope that this study aimed at establishing standardized morphological criteria might serve in future studies dealing with the search for sperm parameters which facilitate an estimation of sperm quality. For this purpose, ejaculates from fertile alpacas were used to evaluate sperm head morphometry by means of the Sperm-Class Analyzer (SCA) computer-aided image analysis system. We defined three morphological categories according to sperm head size (normal 50%, small 26%, large 24%) and five categories according to sperm head shape (normal 47%, pyriform 3%, short 20%, round 1%, long 29%). Sperm classification according to shape was performed by first morphometrically characterizing sperm heads clearly falling into each of the shape categories. Thereafter, discriminant analysis was performed on the data from these typical sperm heads and the resulting classification functions were used to categorize 2,200 spermatozoa from 11 alpacas. Classification of sperm heads by this method agreed in 88% of the cases with most of the misclassifications being due to pyriform heads classified as long heads. Morphometric values obtained from samples of 50, 100, 150, 175 and 200 sperm heads were compared. At least 150 sperm heads should be evaluated to overcome sample size influence on sperm measurements. Significant differences in sperm morphometry were found between individuals (CV for morphometric parameters ranging from 1.3 to 13.0) and there were marked differences in the sperm morphological composition of the ejaculates. Within-animal CV ranged from 4.7 to 17.8 thus showing the high degree of sperm polymorphism present in the alpaca ejaculate.
The authors present their experience in the use of an atlas-based computer system for preoperative functional neurosurgery planning and postoperative analysis. It has also some potential for intraoperative support. The system is based on a deformable electronic version of "Atlas of Stereotaxy of the Human Brain" by Schaltenbrand and Wahren. This atlas is used for interactive segmentation and labelling of clinical data in two- and three dimensions, and for definition of stereotactic targets. The Schaltenbrand-Wahren atlas microseries are digitized, enhanced, segmented, labelled, aligned and organized into atlas volumes. They are mutually preregistered, and three-dimensional models of the structures are constructed. The atlas may be interactively registered with an actual patient's data. A computer system is developed which provides data interpolation, reformatting, registration, visualization, navigation, mensuration and path display and editing in two- and three dimensions. The system increases the accuracy of target definition, reduces the time of planning and the time of the procedure itself. It also constitutes a research platform for the construction of more advanced neurosurgery supporting tools and brain atlases.
The use of Doppler indices of tumor vascularity as markers for malignancy has been a subject of debate, with inconsistencies in specificity and sensitivity as well as in the diagnostic cut-off values. Part of the discrepancy might be explained by the limited number of vessels selected within the tumor for Doppler evaluation. A typical sample of three or four vessels may not be sufficiently large for correct representation of the vasculature of the tumor, which may contain many vessels. The existing conventional Doppler (color and spectral) techniques clearly have limitations. We present here a novel Doppler modality, namely two-dimensional spectral Doppler imaging (SDI), which allows the acquisition of many Doppler spectra within a few seconds. The operator selects a region of interest within a color Doppler image. The Doppler sequence is initiated and the entire selected region is automatically scanned for about 20 s until all spectral Doppler data have been acquired and processed. The system computer generates a color-coded map of the desired Doppler indices overlaid on the gray-scale image. The system also displays a cumulative histogram or a table of the requested Doppler index from all the sections in the tissue. The whole process is automatically performed by the system computer, without any need for operator intervention. The system provides the examiner with a 'fishing net' for Doppler indices, instead of the 'fishing hook' used in current techniques.